Barriers of Urban Mobility

Gergő Pintér1 and Balázs Lengyel1,2,3

1ANETI Lab, Corvinus University of Budapest
2ANETI Lab, HUN-REN Center for Economic and Regional Sciences
3Institute for Data Analytics and Information Systems, Corvinus University of Budapest

5 September 2024

motivation

all roads lead to Rome

motivation

Nagykőrösi road, Budapest by vst via Mapillary CC BY-SA 4.0

amenities enter the equation

[1]

motivation

A wildlife overpass built over Highway 38 in Israel by Hagai Agmon-Snir | CC BY-SA 4.0

mobile positioning data

  • collected from various, unspecified smartphone apps
    • timestamp, user ID, location
    • GPS-based location
  • pings are clustered into stops [1]
    • using Infostop algorithm [2]
    • where some time was spent

building a network

the blocks are considered nodes
connected if a user had consecutive stops in two blocks within a day

community detection

  • using the network built from the stops
  • Louvain community detection is applied
    • with different resolution values
    • executed 10 times for each resolution
Louvain communities (resolution 2.5)

Louvain community detection - resolution 2.5

infrastructural barriers: primary and secondary (dotted) roads
administrative boundaries: districts and neighborhood (dotted)

barrier crossing ratio

BCRγbarrier=1n∑mCB∑mCB×CCγ BCR_{\gamma}^{barrier} = \dfrac{1}{n} \frac{ \sum_{m} \text{{CB}} }{ \sum_{m} \text{{CB}} \times \text{{CC}}_{\gamma} }

  • m is the number of mobility edges
  • γ\gamma is the resolution
  • n is the number of iterations at resolution γ\gamma

by barrier types:

  • district
  • neighborhood
  • primary roads
  • secondary
  • railways
  • river

thanks for the attention!

Gergő Pintér, gergo.pinter @ uni-corvinus.hu, @pintergreg

this presentation is available online: pintergreg.github.io/ccs24

already available on arXiv:

references

[1]
S. Juhász et al., “Amenity complexity and urban locations of socio-economic mixing,” EPJ Data Science, vol. 12, no. 1, p. 34, 2023.
[2]
U. Aslak and L. Alessandretti, “Infostop: Scalable stop-location detection in multi-user mobility data,” arXiv preprint arXiv:2003.14370, 2020.